Journal articles: 'Autonomous Driving'

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Poledna, S., F. Eichler, and P. Schöggl. "Autonomous Driving." Sonderprojekte ATZ/MTZ 24, S1 (August 2019): 47. http://dx.doi.org/10.1007/s41491-019-0029-8.

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Walch, Marcel, Kristin Mühl, Martin Baumann, and Michael Weber. "Autonomous Driving." International Journal of Mobile Human Computer Interaction 9, no. 2 (April 2017): 58–74. http://dx.doi.org/10.4018/ijmhci.2017040104.

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Autonomous vehicles will need de-escalation strategies to compensate when reaching system limitations. Car-driver handovers can be considered one possible method to deal with system boundaries. The authors suggest a bimodal (auditory and visual) handover assistant based on user preferences and design principles for automated systems. They conducted a driving simulator study with 30 participants to investigate the take-over performance of drivers. In particular, the authors examined the effect of different warning conditions (take-over request only with 4 and 6 seconds time budget vs. an additional pre-cue, which states why the take-over request will follow) in different hazardous situations. Their results indicated that all warning conditions were feasible in all situations, although the short time budget (4 seconds) was rather challenging and led to a less safe performance. An alert ahead of a take-over request had the positive effect that the participants took over and intervened earlier in relation to the appearance of the take-over request. Overall, the authors' evaluation showed that bimodal warnings composed of textual and iconographic visual displays accompanied by alerting jingles and spoken messages are a promising approach to alert drivers and to ask them to take over.

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Fuchs, Andreas. "Autonomous Driving." ATZoffhighway worldwide 11, no. 1 (March 2018): 3. http://dx.doi.org/10.1007/s41321-018-0013-3.

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Salow, Holger. "Autonomous driving." ATZ worldwide 110, no. 1 (January 2008): 14–18. http://dx.doi.org/10.1007/bf03224976.

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Han, Joong-hee, Chi-ho Park, Young Yoon Jang, Ja Duck Gu, and Chan Young Kim. "Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment." Sensors 22, no. 1 (December 24, 2021): 114. http://dx.doi.org/10.3390/s22010114.

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To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively.

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Köster, Oliver. "Mandatory Autonomous Driving?" ATZelectronics worldwide 14, no. 3 (March 2019): 66. http://dx.doi.org/10.1007/s38314-019-0016-6.

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Tyler, Neil. "Safer Autonomous Driving." New Electronics 51, no. 18 (October 9, 2018): 8. http://dx.doi.org/10.12968/s0047-9624(23)60679-0.

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STAYTON, ERIK, MELISSA CEFKIN, and JINGYI ZHANG. "Autonomous Individuals in Autonomous Vehicles: The Multiple Autonomies of Self-Driving Cars." Ethnographic Praxis in Industry Conference Proceedings 2017, no. 1 (November 2017): 92–110. http://dx.doi.org/10.1111/1559-8918.2017.01140.

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Baber, J., J. Kolodko, T. Noel, M. Parent, and L. Vlacic. "Cooperative autonomous driving - Intelligent vehicles sharing city roads cooperative autonomous driving." IEEE Robotics & Automation Magazine 12, no. 1 (March 2005): 44–49. http://dx.doi.org/10.1109/mra.2005.1411418.

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Hurair, Mohammad, Jaeil Ju, and Junghee Han. "Environmental-Driven Approach towards Level 5 Self-Driving." Sensors 24, no. 2 (January 12, 2024): 485. http://dx.doi.org/10.3390/s24020485.

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As technology advances in almost all areas of life, many companies and researchers are working to develop fully autonomous vehicles. Such level 5 autonomous driving, unlike levels 0 to 4, is a driverless vehicle stage and so the leap from level 4 to level 5 autonomous driving requires much more research and experimentation. For autonomous vehicles to safely drive in complex environments, autonomous cars should ensure end-to-end delay deadlines of sensor systems and car-controlling algorithms including machine learning modules, which are known to be very computationally intensive. To address this issue, we propose a new framework, i.e., an environment-driven approach for autonomous cars. Specifically, we identify environmental factors that we cannot control at all, and controllable internal factors such as sensing frequency, image resolution, prediction rate, car speed, and so on. Then, we design an admission control module that allows us to control internal factors such as image resolution and detection period to determine whether given parameters are acceptable or not for supporting end-to-end deadlines in the current environmental scenario while maintaining the accuracy of autonomous driving. The proposed framework has been verified with an RC car and a simulator.

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Knabl, Florian, and Lars Mesow. "Autonomes Fahren im Kleinformat Audi Autonomous Driving Cup." Sonderprojekte ATZ/MTZ 22, S2 (December 2017): 26–29. http://dx.doi.org/10.1007/s41491-017-0006-z.

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Steck, Felix, Viktoriya Kolarova, Francisco Bahamonde-Birke, Stefan Trommer, and Barbara Lenz. "How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 46 (April 6, 2018): 11–20. http://dx.doi.org/10.1177/0361198118757980.

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Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent traveling as they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and little empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated-choice experiments. Two use cases were addressed – a privately owned, and a shared autonomous vehicle – compared with other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in VTTS for commuting trips. It was found that driving autonomously in a privately owned vehicle might reduce the VTTS by 31% compared with driving manually, and is perceived similarly to in-vehicle time in public transportation. Furthermore, riding in a shared autonomous vehicle is perceived 10% less negatively than driving manually. The study provides important insights into VTTS by autonomous driving for commuting trips and could be a base for future research to build upon.

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V S, Amar. "Autonomous Driving using CNN." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3633–36. http://dx.doi.org/10.22214/ijraset.2021.35771.

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Human beings are currently addicted to automation and robotics technologies. The state-of-the-art in deep learning technologies and AI is the subject of this autonomous driving. Driving with automated driving systems promises to be safe, enjoyable, and efficient.. It is preferable to train in a virtual environment first and then move to a real-world one. Its goal is to enable a vehicle to recognise its surroundings and navigate without the need for human intervention. The raw pixels from a single front-facing camera were directly transferred to driving commands using a convolution neural network (CNN). This end-to-end strategy proved to be remarkably effective, The system automatically learns internal representations of the essential processing stages such as detecting useful road components using only the human steering angle as the training signal. We never expressly taught it to recognise the contour of roadways, for example. In comparison to explicit issue decomposition, such as lane marking detection, Our end-to-end solution optimises all processing processes at the same time, including path planning and control. We believe that this will lead to improved performance and smaller systems in the long run. Internal components will self-optimize to maximise overall system performance, resulting in improved performance.

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M R, Prajwal. "Self-Driving Autonomous Car." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 260–63. http://dx.doi.org/10.22214/ijraset.2020.30866.

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Franke, U., D. Gavrila, S. Gorzig, F. Lindner, F. Puetzold, and C. Wohler. "Autonomous driving goes downtown." IEEE Intelligent Systems 13, no. 6 (November 1998): 40–48. http://dx.doi.org/10.1109/5254.736001.

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Chen, Shu-Ching. "Multimedia for Autonomous Driving." IEEE MultiMedia 26, no. 3 (July 1, 2019): 5–8. http://dx.doi.org/10.1109/mmul.2019.2935397.

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Tian, Jilei, Alvin Chin, and Halim Yanikomeroglu. "Connected and Autonomous Driving." IT Professional 20, no. 6 (November 1, 2018): 31–34. http://dx.doi.org/10.1109/mitp.2018.2876928.

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Reichenbach, Michael. "Autonomous Driving and Digitization." ATZ worldwide 120, no. 1 (January 2018): 16–17. http://dx.doi.org/10.1007/s38311-017-0175-0.

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Perez, Manuel. "Autonomous driving in NMR." Magnetic Resonance in Chemistry 55, no. 1 (November 17, 2016): 15–21. http://dx.doi.org/10.1002/mrc.4546.

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AL - SMADI, Adnan, Sara AL - ESSA, Shaima MOMANI, and Haneen BANI - SALAMEH. "Vehicle Autonomous Driving System." Eurasia Proceedings of Science Technology Engineering and Mathematics 28 (August 15, 2024): 408–16. http://dx.doi.org/10.55549/epstem.1523618.

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Autonomous vehicles or self-driving cars industry and technology have played an important role in research industry and automotive industry. Autonomous cars are those vehicles have the ability to sense there surrounding and navigate and drive themselves on roads through traffic without human interventions. In other words, they can move from one location to another one without human interaction. In this paper, an autonomous vehicle system prototype is proposed. The vehicle is able to sense its surrounding and keep going on the road on its own through traffic and other barriers such as people and traffic lights. That is, the vehicles are able to drive and detect the road signals and take decision accordingly, whether to continue or to make a turn. The proposed system uses raspberry pi microcontroller and ultrasonic sensors to detect any object, obstacle, or pedestrians in front of the vehicle and to measure the distance. In addition, a raspberry pi camera is connected to the raspberry pi to continuously take pictures of the road. These pictures will be analyzed by the raspberry pi microcontroller. The vehicle is able to reach its destination safely. A self-driving car prototype has been designed and implemented. The porotype autonomous vehicle system was tested and performed as expected.

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Manawadu, Udara Eshan, Masaaki Ishikawa, Mitsuhiro Kamezaki, and Shigeki Sugano. "Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator." Journal of Robotics and Mechatronics 27, no. 6 (December 18, 2015): 660–70. http://dx.doi.org/10.20965/jrm.2015.p0660.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (January 20, 2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Car to control the car, and the autonomous driving-related algorithms were implemented in a separate AI server, and they communicated with the message-based ROS protocol. In addition, those who want to develop an autonomous driving system can easily attach desired sensors to the RC-Car, increasing scalability. In this paper, almost all algorithms related to autonomous driving have been implemented. A simple autonomous driving RC-Car system was actually implemented and operation was verified by designing and implementing algorithms such as lane recognition, driving along the lane, obstacle detection and stopping, traffic light recognition, driving between smooth and sharp curves, and autonomous parking. In sharp curves, the angle of the lane was tracked in a short period to prevent the vehicle from crossing the lane. In addition, we developed an Android app that can manually control the car and monitor the video from the camera in time. This study presented and solved various difficulties that could not be known by developing an autonomous driving algorithm using simulators.

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Jain, Kavita, Sean Pereira, Vrishab Shetty, Lysandra D’Souza, and Akash Mathew. "Autonomous Vehicle." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 1363–69. http://dx.doi.org/10.22214/ijraset.2023.56780.

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Abstract: Self-driving cars are a rapidly advancing technology that is poised to revolutionize the way people travel. The purpose of the self-driving car project is to create a vehicle that can operate autonomously, without the need for a human driver. This technology is being developed to increase safety on the road, reduce traffic congestion, and provide a more efficient and convenient mode of transportation. The project involves a combination of hardware and software development, including sensors, cameras, and machine learning algorithms. Self-driving cars have the potential to reduce the number of accidents caused by human error, improve traffic flow, and provide greater mobility for individuals who are unable to drive. While there are still many technical and regulatory challenges to overcome, self-driving cars represent an exciting future for the automotive industry and society as a whole

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Ren, Weixi, Bo Yu, Yuren Chen, and Kun Gao. "Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach." International Journal of Environmental Research and Public Health 19, no. 18 (September 9, 2022): 11358. http://dx.doi.org/10.3390/ijerph191811358.

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Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors’ flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

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Ansari, Hashim Shakil, and Goutam R. "Autonomous Driving using Deep Reinforcement Learning in Urban Environment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1573–75. http://dx.doi.org/10.31142/ijtsrd23442.

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Kolarova, Viktoriya. "Exploring the Elements and Determinants of the Value of Time for Manual Driving and Autonomous Driving using a Qualitative Approach." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 22, 2020): 542–52. http://dx.doi.org/10.1177/0361198120953770.

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Autonomous driving is expected to change individual travel behavior significantly. The main reason postulated is an increase in comfort and feasibility of on-board activities which will potentially change the way people perceive time spent in a vehicle and consequently their mode preferences. Understanding how value of time (VoT) might change and what will determine such change can be crucial when assessing the impact of vehicle automation. Recent studies address potential changes that automation might have on VoT based on analyses of time use and perception in current modes of transport or focusing only on the utility of driving autonomously. However, there is a lack of research addressing both—the utility of car driving compared with the utility of riding autonomously—from the user perspective. To address this research question, focus group discussions with car drivers were conducted. The data was analyzed using a thematic qualitative text analysis. The results suggest that the utility of car driving today, including aspects of driving pleasure, various (passive) activities performed in the car, and also driving as an activity itself, will counterbalance to a certain extent the effect of the benefits of autonomous driving, such as improved travel experience and feasibility of activities. Moreover, context-related and individual characteristics shape these effects. This paper summarizes the main study results, including potential short- and long-term travel behavior changes resulting from the availability of autonomous driving. Lastly, implications from the qualitative research for quantitative studies on value of travel time savings for autonomous vehicles are discussed.

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Kwon, Yonghun, Woojae Kim, and Inbum Jung. "Neural Network Models for Driving Control of Indoor Autonomous Vehicles in Mobile Edge Computing." Sensors 23, no. 5 (February 25, 2023): 2575. http://dx.doi.org/10.3390/s23052575.

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Mobile edge computing has been proposed as a solution for solving the latency problem of traditional cloud computing. In particular, mobile edge computing is needed in areas such as autonomous driving, which requires large amounts of data to be processed without latency for safety. Indoor autonomous driving is attracting attention as one of the mobile edge computing services. Furthermore, it relies on its sensors for location recognition because indoor autonomous driving cannot use a GPS device, as is the case with outdoor driving. However, while the autonomous vehicle is being driven, the real-time processing of external events and the correction of errors are required for safety. Furthermore, an efficient autonomous driving system is required because it is a mobile environment with resource constraints. This study proposes neural network models as a machine-learning method for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the range data measured with the LiDAR sensor. We designed six neural network models to be evaluated according to the number of input data points. In addition, we made an autonomous vehicle based on the Raspberry Pi for driving and learning and an indoor circular driving track for collecting data and performance evaluation. Finally, we evaluated six neural network models in terms of confusion matrix, response time, battery consumption, and driving command accuracy. In addition, when neural network learning was applied, the effect of the number of inputs was confirmed in the usage of resources. The result will influence the choice of an appropriate neural network model for an indoor autonomous vehicle.

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Han, Joong-hee, Chi-ho Park, and Young Yoon Jang. "Development of Location-Data-Based Orchard Passage Map Generation Method." Sensors 24, no. 3 (January 25, 2024): 795. http://dx.doi.org/10.3390/s24030795.

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Currently, pest control work using speed sprayers results in increasing numbers of safety accidents such as worker pesticide poisoning and rollover of vehicles during work. To address this, there is growing interest in autonomous driving technology for speed sprayers. To commercialize and rapidly expand the use of self-driving speed sprayers, an economically efficient self-driving speed sprayer using a minimum number of sensors is essential. This study developed an orchard passage map using location data acquired from positioning sensors to generate autonomous driving paths, without installing additional sensors. The method for creating the orchard passage map presented in this study was to create paths using location data obtained by manually driving the speed sprayer and merging them. In addition, to apply the orchard passage map when operating autonomously, a method is introduced for generating an autonomous driving path for the work start point movement path, work path, and return point movement path.

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Shin, Seok-San, Ho-Joon Kang, and Seong-Jin Kwon. "A Study on Data Analysis for Improving Driving Safety in Field Operational Test (FOT) of Autonomous Vehicles." Machines 10, no. 9 (September 7, 2022): 784. http://dx.doi.org/10.3390/machines10090784.

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In this study, an autonomous driving test was conducted from the perspective of FOT (field operational test). For data analysis and improvement methods, scenarios for FOT were classified and defined by considering autonomous driving level (SAE J3016) and the viewpoints of the vehicle, driver, road, environment, etc. To obtain data from FOT, performance indicators were selected, a data collection environment was implemented in the test cases, and driving roads were selected to obtain driving data from the vehicle while it was driven on an actual road. In the pilot FOT course, data were collected in various driving situations using a test vehicle, and the effect of autonomous driving-related functions on improving driving safety was studied through data analysis of discovered major events.

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Mühl, Kristin, Christoph Strauch, Christoph Grabmaier, Susanne Reithinger, Anke Huckauf, and Martin Baumann. "Get Ready for Being Chauffeured." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 8 (September 9, 2019): 1322–38. http://dx.doi.org/10.1177/0018720819872893.

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Objective We investigated passenger’s trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment. Background The passenger’s perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger’s appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans. Method We conducted an exploratory field study using an autonomous research vehicle ( N = 11) and a follow-up experimental driving simulation ( N = 24). Participants were driven on the same course by a human and an autonomous agent sitting on a passenger seat. Skin conductance, trust, and qualitative characteristics of the perceived driving situation were assessed. In addition, the effect of driving style (defensive vs. sporty) was evaluated in the simulator. Results Both investigations revealed a close relation between subjective trust ratings and skin conductance, with increased trust and by trend reduced arousal for human compared with automation in control. Even though driving behavior was equivalent in the simulator when being driven by human and automation, passengers most preferred and trusted the human-defensive driver. Conclusion Individual preferences for driving style and human or autonomous vehicle control influence trust and subjective driving characterizations. Application The findings are applicable in human-automation research, reminding to not neglect subjective attributions and psychophysiological reactions as a result of ascribed control duties in relation to specific execution characteristics.

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Zhang, Tingyu. "The application of deep learning in autonomous driving." Applied and Computational Engineering 50, no. 1 (March 25, 2024): 144–48. http://dx.doi.org/10.54254/2755-2721/50/20241379.

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Autonomous driving technology is currently a globally prominent subject, with its relevance and impact readily apparent. Leveraging advanced sensors, sophisticated algorithms, and state-of-the-art computer vision techniques, autonomous vehicles can autonomously navigate, mitigate traffic accidents, and alleviate urban congestion. Furthermore, this technology is poised to accelerate innovation within the automotive sector, drive industrial advancement, and enhance people's convenience and safety in their travel experiences. The importance of autonomous driving technology is not only reflected in its own advantages, but also in its ability to lead the development direction of intelligent transportation in the future, helping to promote the development of intelligent transportation systems, realize the intelligent interconnection between vehicles and vehicles, vehicles and road infrastructure, and further enhance the safety and convenience of traffic travel. Therefore, autonomous driving technology is a significant innovation that will bring a better and more convenient future for mankind. In this study, a method based on sensor internal and external parameter calibration transformation matrix, road target detection algorithm, automatic vehicle detection method, pedestrian intention prediction technology and automatic pedestrian recognition system are analyzed, which are applied to object detection and obstacle avoidance. This article provides a good overview of the field of autonomous driving.

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Rößger, Peter. "Autonomous Driving How Much Autonomy Driving Does Stand." ATZelektronik worldwide 10, no. 2 (April 2015): 26–29. http://dx.doi.org/10.1007/s38314-015-0514-0.

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Chen, Yuanzhe. "World models for autonomous driving." Applied and Computational Engineering 75, no. 1 (July 5, 2024): 14–18. http://dx.doi.org/10.54254/2755-2721/75/20240500.

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Advancements in autonomous driving have been achieved due to the increasing use of artificial intelligence. The existing autonomous driving technology is insufficient to handle intricate traffic conditions. In autonomous driving, the world model is a crucial and innovative technology. Utilizing sensors and pre-existing knowledge, a world model can enhance the autonomous driving system's comprehension of the surroundings, offer essential data for future judgments, and enhance the system's resilience. The paper employs literature analysis and review methods to investigate the research and implementation of world models in autonomous driving, including environment perception and modeling, path planning, decision-making, and safety. This study examines the use of artificial intelligence technology in autonomous driving and analyzes the research and application of the world model in this field. It offers new insights for addressing challenging scenarios in autonomous driving and enhancing the safety of the system.

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Yang, Diange, Xinyu Jiao, Kun Jiang, and Zhong Cao. "Driving Space for Autonomous Vehicles." Automotive Innovation 2, no. 4 (December 2019): 241–53. http://dx.doi.org/10.1007/s42154-019-00081-1.

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AbstractDriving space for autonomous vehicles (AVs) is a simplified representation of real driving environments that helps facilitate driving decision processes. Existing literatures present numerous methods for constructing driving spaces, which is a fundamental step in AV development. This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions: how to reconstruct the driving environment, and how to make driving decisions within the constructed driving space. Furthermore, the advantages and disadvantages of different types of driving space are analyzed. The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.

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Jin, Cance. "Artificial intelligence and autonomous driving." Applied and Computational Engineering 20, no. 1 (October 23, 2023): 165–69. http://dx.doi.org/10.54254/2755-2721/20/20231092.

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With the rapid development of science and technology, artificial intelligence and autonomous driving technology are becoming a hot topic in today's society. The integration of artificial intelligence and driving systems satisfies this requirement effectively and produces a new driving technology for the sake of traffic safety and a better driving experience. To increase driving safety and comfort, computer calculations are utilized to aid the driver or eliminate the interference of human variables. First, this study introduce the basic concepts and principles of artificial intelligence and autonomous driving, as well as their importance in practical applications. Secondly, this paper discuss in detail the key technologies of artificial intelligence in autonomous driving, including the application of perception, decision making, and control. Finally, his essay explore the challenges facing artificial intelligence and autonomous driving, including technical challenges, legal and ethical considerations. Through the research of this paper, we can better understand the relationship between artificial intelligence and autonomous driving, and provide reference and guidance for future development.

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Khan, Manzoor Ahmed. "Intelligent Environment Enabling Autonomous Driving." IEEE Access 9 (2021): 32997–3017. http://dx.doi.org/10.1109/access.2021.3059652.

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ENDO, Kaoru. "Autonomous Driving and Social Ethics." TRENDS IN THE SCIENCES 25, no. 5 (May 1, 2020): 5_48–5_51. http://dx.doi.org/10.5363/tits.25.5_48.

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Hou, Yijie, Chengshun Wang, Junhong Wang, Xiangyang Xue, Xiaolong Luke Zhang, Jun Zhu, Dongliang Wang, and Siming Chen. "Visual Evaluation for Autonomous Driving." IEEE Transactions on Visualization and Computer Graphics 28, no. 1 (January 2022): 1030–39. http://dx.doi.org/10.1109/tvcg.2021.3114777.

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Ebert, Christof, Michael Weyrich, Benjamin Lindemann, and Sarada Preethi Chandrasekar. "Systematic Testing for Autonomous Driving." ATZelectronics worldwide 16, no. 3 (March 2021): 18–23. http://dx.doi.org/10.1007/s38314-020-0575-6.

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Salow, Holger, and Karen Tippkötter. "Autonomous Driving with Laser Scanners." ATZautotechnology 7, no. 5 (September 2007): 44–47. http://dx.doi.org/10.1007/bf03247013.

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Sadasivam, Srikumar. "Autonomous driving — what drives it?" Auto Tech Review 4, no. 9 (September 2015): 22–27. http://dx.doi.org/10.1365/s40112-015-0978-6.

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Cho, Eunae, and Yoonhyuk Jung. "Consumers’ understanding of autonomous driving." Information Technology & People 31, no. 5 (October 1, 2018): 1035–46. http://dx.doi.org/10.1108/itp-10-2017-0338.

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Purpose The purpose of this paper is to explore consumers’ understanding of autonomous driving by comparing perceptions of occasional drivers (ODs) and frequent drivers (FDs). Design/methodology/approach Data were gathered through semi-structured interviews with 41 drivers. Their responses were categorized into thematic categories or topics on the basis of content analysis, and the topics were structured based on the core-periphery model. Finally, the authors visualized the structure on a perceptual map by adopting a maximum tree approach. Findings Respondents’ understanding of autonomous driving were categorized into 10 topics. There were significant differences in topics and their relationships between ODs and FDs. Findings also show that FD can better detect hazardousness from autonomous driving environments than ODs. Research limitations/implications Differently from prior studies’ focus on its technological aspect and some derived benefits, the study examines it from the viewpoint of consumers, who are critical participants in the dissemination of autonomous driving. Practical implications The findings suggest that rather than focusing on developing the highest level of autonomous cars, developing in an evolutionary way by adding automated functions to existing cars can be the better strategy to dominate the autonomous vehicle market. Originality/value This study is a pioneering work in that it can be an initial empirical work on autonomous driving from the customer standpoint.

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RAKSINCHAROENSAK, Pongsathorn. "Evolution of Autonomous Driving Technology." Proceedings of Mechanical Engineering Congress, Japan 2018 (2018): K18100. http://dx.doi.org/10.1299/jsmemecj.2018.k18100.

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Liu, Shaoshan, Jie Tang, Zhe Zhang, and Jean-Luc Gaudiot. "Computer Architectures for Autonomous Driving." Computer 50, no. 8 (2017): 18–25. http://dx.doi.org/10.1109/mc.2017.3001256.

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Kim, Junsung, Ragunathan (Raj) Rajkumar, and Markus Jochim. "Towards dependable autonomous driving vehicles." ACM SIGBED Review 10, no. 1 (February 2013): 29–32. http://dx.doi.org/10.1145/2492385.2492390.

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Loh, Wulf, and Catrin Misselhorn. "Autonomous Driving and Perverse Incentives." Philosophy & Technology 32, no. 4 (July 16, 2018): 575–90. http://dx.doi.org/10.1007/s13347-018-0322-6.

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Solomon, Andreea, and Ronald Kaempf. "Testing Solutions for Autonomous Driving." ATZ worldwide 119, no. 9 (August 25, 2017): 56–59. http://dx.doi.org/10.1007/s38311-017-0085-1.

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Barabás, I., A. Todoruţ, N. Cordoş, and A. Molea. "Current challenges in autonomous driving." IOP Conference Series: Materials Science and Engineering 252 (October 2017): 012096. http://dx.doi.org/10.1088/1757-899x/252/1/012096.

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Devi, T. Kirthiga, Akshat Srivatsava, Kritesh Kumar Mudgal, Ranjnish Raj Jayanti, and T. Karthick. "Behaviour Cloning for Autonomous Driving." Webology 17, no. 2 (December 21, 2020): 694–705. http://dx.doi.org/10.14704/web/v17i2/web17061.

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The objective of this project is to automate the process of driving a car. The result of this project will surely reduce the number of hazards happening everyday. Our world is in progress and self driving car is on its way to reach consumer‟s door-step but the big question still lies that will people accept such a car which is fully automated and driverless. The idea is to create an autonomous Vehicle that uses only some sensors (collision detectors, temperature detectors etc.) and camera module to travel between destinations with minimal/no human intervention. The car will be using a trained Convolutional Neural Network (CNN) which would control the parameters that are required for smoothly driving a car. They are directly connected to the main steering mechanism and the output of the deep learning model will control the steering angle of the vehicle. Many algorithms like Lane Detection, Object Detection are used in tandem to provide the necessary functionalities in the car.

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Hartmann, Gabriel, Zvi Shiller, and Amos Azaria. "Competitive Driving of Autonomous Vehicles." IEEE Access 10 (2022): 111772–83. http://dx.doi.org/10.1109/access.2022.3215984.

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Journal articles on the topic 'Autonomous Driving'

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